1 Transects

Plant Flowers Date lon lat ele Month Year julian
Glossoloma oblongicalyx 4 2015-10-19 -78.59093 0.130838 2270 October 2015 292
Gasteranthus quitensis 2 2016-10-17 -78.59770 0.120070 1940 October 2016 291
Kohleria affinis 1 2016-12-13 -78.59534 0.126746 2110 December 2016 348
Columnea ciliata 3 2014-02-27 -78.59934 0.116682 1960 February 2014 58
Columnea medicinalis 1 2014-04-23 -78.59372 0.128700 2130 April 2014 113
Drymonia teuscheri 3 2016-07-28 -78.59245 0.129393 2200 July 2016 210

2 Interactions

3 Phylogeny

4 Traits

Plant Flowers Date lon lat ele Month Year julian
Glossoloma oblongicalyx 4 2015-10-19 -78.59093 0.130838 2270 October 2015 292
Gasteranthus quitensis 2 2016-10-17 -78.59770 0.120070 1940 October 2016 291
Kohleria affinis 1 2016-12-13 -78.59534 0.126746 2110 December 2016 348
Columnea ciliata 3 2014-02-27 -78.59934 0.116682 1960 February 2014 58
Columnea medicinalis 1 2014-04-23 -78.59372 0.128700 2130 April 2014 113
Drymonia teuscheri 3 2016-07-28 -78.59245 0.129393 2200 July 2016 210

4.0.1 Total Flowers

4.1 Peak date

As range

4.2 Infer absences

4.3 Species elevation ranges

4.4 Species by transect matrix

Check date integrity

5 Count model of species phenology

## sink("model/Poisson_baseline.jags")
## cat("
##     model {
##     
##     for (x in 1:Dates){
##     for (y in 1:Plants){
##     #Observation of a flowering plant
##     Y[x,y] ~ dpois(p[x,y])
##     log(p[x,y]) <- alpha[y]
##     
##     #Residuals
##     discrepancy[x,y] <- pow(Y[x,y] - p[x,y],2)
##     
##     #Assess Model Fit
##     Ynew[x,y] ~ dpois(p[x,y])
##     discrepancy.new[x,y]<-pow(Ynew[x,y] - p[x,y],2)
##     }
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for (x in 1:NewDates){
##     for (y in 1:Plants){
##     
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x,y] ~ dpois(p_new[x,y])
##     log(p_new[x,y])<-alpha[y]
##     
##     #squared predictive error
##     pred_error[x,y] <- pow(Ypred[x,y] - prediction[x,y],2)
##     }
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept
##     #Intercept flowering count
##     alpha[j] ~ dnorm(0,0.001)
##     
##     } 
## 
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2926
##    Unobserved stochastic nodes: 3444
##    Total graph size: 14251
## 
## Initializing model

6 Get Chains

6.0.1 Evaluate convergence

6.0.2 Posterior estimates

7 Phylogeny

7.1 Attraction

## sink("model/Poisson_attraction.jags")
## cat("
##     model {
##     
##     for (x in 1:Dates){
##       for (y in 1:Plants){
##         #Observation of a flowering plant
##         Y[x,y] ~ dpois(p[x,y])
##         log(p[x,y]) <- alpha[y] + e[x,y]
##   
##         #Residuals
##         discrepancy[x,y] <- pow(Y[x,y] - p[x,y],2)
##         
##         #Assess Model Fit
##         Ynew[x,y] ~ dpois(p[x,y])
##         discrepancy.new[x,y]<-pow(Ynew[x,y] - p[x,y],2)
##       }
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for (x in 1:NewDates){
##       for (y in 1:Plants){
##     
##       #predict value
##       
##       #Observation - probability of flowering
##       prediction[x,y] ~ dpois(p_new[x,y])
##       log(p_new[x,y])<-alpha[y] + e_new[x,y]
##       
##       #squared predictive error
##       pred_error[x,y] <- pow(Ypred[x,y] - prediction[x,y],2)
##       }
## }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(k in 1:Dates){
##       e[k,1:Plants] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     #For each prediction
##     for(k in 1:NewDates){
##       e_new[k,1:Plants] ~ dmnorm(zeros,tauC[,])
##     }
## 
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=inverse(vCov*gamma)
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept
##     #Intercept flowering count
##     alpha[j] ~ dnorm(0,0.001)
##     
##     } 
##     
##     #Autocorrelation priors
##     gamma ~ dunif(0,20)
##     
##     #Strength of covariance decay
##     lambda_cov = 1
##     omega ~ dbeta(1,1)
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2926
##    Unobserved stochastic nodes: 3691
##    Total graph size: 30586
## 
## Initializing model

8 Get Chains

8.0.1 Evaluate convergence

8.0.2 Posterior estimates

Mean phylogenetic covariance

8.1 Decay in phylogenetic attraction

8.2 Repulsion

## sink("model/Poisson_repulsion.jags")
## cat("
##     model {
##     
##     for (x in 1:Dates){
##     for (y in 1:Plants){
##     #Observation of a flowering plant
##     Y[x,y] ~ dpois(p[x,y])
##     log(p[x,y]) <- alpha[y] + e[x,y]
##     
##     #Residuals
##     discrepancy[x,y] <- pow(Y[x,y] - p[x,y],2)
##     
##     #Assess Model Fit
##     Ynew[x,y] ~ dpois(p[x,y])
##     discrepancy.new[x,y]<-pow(Ynew[x,y] - p[x,y],2)
##     }
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for (x in 1:NewDates){
##     for (y in 1:Plants){
##     
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x,y] ~ dpois(p_new[x,y])
##     log(p_new[x,y])<-alpha[y] + e_new[x,y]
##     
##     #squared predictive error
##     pred_error[x,y] <- pow(Ypred[x,y] - prediction[x,y],2)
##     }
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(k in 1:Dates){
##     e[k,1:Plants] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     #For each prediction
##     for(k in 1:NewDates){
##     e_new[k,1:Plants] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma    
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept
##     #Intercept flowering count
##     alpha[j] ~ dnorm(0,0.001)
##     
##     } 
##     
##     #Autocorrelation priors
##     gamma ~ dunif(0,20)
##     
##     #Strength of covariance decay
##     lambda_cov = 1
##     omega ~ dbeta(1,1)
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2926
##    Unobserved stochastic nodes: 3691
##    Total graph size: 30585
## 
## Initializing model

9 Get Chains

9.0.1 Evaluate convergence

9.0.2 Posterior estimates

Mean phylogenetic covariance martix

9.1 Decay in phylogenetic repulsion

10 Traits

10.1 Trait Attraction

## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2926
##    Unobserved stochastic nodes: 3691
##    Total graph size: 30742
## 
## Initializing model

11 Get Chains

11.0.1 Evaluate convergence

11.0.2 Posterior estimates

11.1 Decay in trait attraction

11.2 Repulsion

## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2926
##    Unobserved stochastic nodes: 3691
##    Total graph size: 30741
## 
## Initializing model

12 Get Chains

12.0.1 Evaluate convergence

12.0.2 Posterior estimates

12.1 Decay in trait repulsion

13 Interaction

13.1 Attraction

## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2926
##    Unobserved stochastic nodes: 3691
##    Total graph size: 30742
## 
## Initializing model

13.1.1 Evaluate convergence

13.1.2 Posterior estimates

Mean interaction covariance

13.2 Decay in interaction attraction

13.3 Repulsion

## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2926
##    Unobserved stochastic nodes: 3691
##    Total graph size: 30741
## 
## Initializing model

14 Get Chains

14.0.1 Evaluate convergence

14.0.2 Posterior estimates

Mean interaction covariance martix

14.1 Decay in interaction repulsion

15 Model Comparison

15.1 Alpha

The probability of occurrence.

15.2 E: The effect of autocorrelation on mean flowering intensity

15.3 Omega: The magnitude of the effect of autocorrelation on mean flowering occurrence

15.4 Gamma: The variance of the effect of autocorrelation on mean flowering occurrence

15.5 Effect of autocorrelation

15.6 Decay in autocorrelation effect

16 Model Fit

16.1 Bayesian pvalue

## # A tibble: 6 x 2
##   Model                       p
##   <chr>                   <dbl>
## 1 interaction_attraction  0.522
## 2 interaction_repulsion   0.492
## 3 phylogenetic_attraction 0.505
## 4 phylogenetic_repulsion  0.49 
## 5 trait_attraction        0.452
## 6 trait_repulsion         0.525

16.2 Overall

Model mean lower upper
trait_attraction 2.418181 2.084980 2.790639
phylogenetic_repulsion 2.411806 2.089727 2.794331
trait_repulsion 2.391713 2.051739 2.747518
interaction_repulsion 2.388664 2.086966 2.713546
interaction_attraction 2.387773 2.044458 2.761761
phylogenetic_attraction 2.375222 2.060701 2.743851

16.2.1 Without baseline

16.3 By Species

Without baseline

Zoom in

16.4 By date

17 Prediction

17.0.1 Tables

Model mean lower upper
phylogenetic_attraction 16801.08890 174.6443 14849.82192
interaction_attraction 13302.94695 119.6427 17460.92093
trait_attraction 9508.92972 138.7701 22176.49950
trait_repulsion 5178.11078 124.2442 14763.12411
interaction_repulsion 5127.10258 139.3778 16753.41121
phylogenetic_repulsion 4288.24908 143.2137 17409.50813
baseline 39.84858 37.7498 41.95456